
"""simple docstring"""






import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


UpperCAmelCase        =				logging.getLogger(__name__)



class 			lowercase					( lowercase__		):
			lowercase        =     '''summarization'''
			lowercase        =     ['''loss''']
			lowercase        =     ROUGE_KEYS
			lowercase        =     '''rouge2'''






			def __init__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	Dict:





				"""simple docstring"""


				if hparams.sortish_sampler and hparams.gpus > 1:
					lowerCAmelCase												=  False
				elif hparams.max_tokens_per_batch is not None:
					if hparams.gpus > 1:
						raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training'''			)
					if hparams.sortish_sampler:
						raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'''			)

				super().__init__(SCREAMING_SNAKE_CASE_      ,num_labels=SCREAMING_SNAKE_CASE_      ,mode=self.mode      ,**SCREAMING_SNAKE_CASE_			)
				use_task_specific_params(self.model      ,'''summarization'''			)
				save_git_info(self.hparams.output_dir			)
				lowerCAmelCase												=  Path(self.output_dir			) / '''metrics.json'''
				lowerCAmelCase												=  Path(self.output_dir			) / '''hparams.pkl'''
				pickle_save(self.hparams      ,self.hparams_save_path			)
				lowerCAmelCase												=  0
				lowerCAmelCase												=  defaultdict(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.config.model_type
				lowerCAmelCase												=  self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

				lowerCAmelCase												=  {
				    "data_dir": self.hparams.data_dir,
				    "max_source_length": self.hparams.max_source_length,
				    "prefix": self.model.config.prefix or "",
				}
				lowerCAmelCase												=  {
				    '''train''': self.hparams.n_train,
				    '''val''': self.hparams.n_val,
				    '''test''': self.hparams.n_test,
				}
				lowerCAmelCase												=  {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

				lowerCAmelCase												=  {
				    '''train''': self.hparams.max_target_length,
				    '''val''': self.hparams.val_max_target_length,
				    '''test''': self.hparams.test_max_target_length,
				}
				assert self.target_lens["train"] <= self.target_lens["val"], F"""target_lens: {self.target_lens}"""
				assert self.target_lens["train"] <= self.target_lens["test"], F"""target_lens: {self.target_lens}"""
				if self.hparams.freeze_embeds:
					freeze_embeds(self.model			)
				if self.hparams.freeze_encoder:
					freeze_params(self.model.get_encoder()			)
					assert_all_frozen(self.model.get_encoder()			)

				lowerCAmelCase												=  get_git_info()['''repo_sha''']
				lowerCAmelCase												=  hparams.num_workers
				lowerCAmelCase												=  None  # default to config
				if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer      ,SCREAMING_SNAKE_CASE_			):
					lowerCAmelCase												=  self.tokenizer.lang_code_to_id[hparams.tgt_lang]
					lowerCAmelCase												=  self.decoder_start_token_id
				lowerCAmelCase												=  (
				    SeqaSeqDataset if hasattr(self.tokenizer      ,'''prepare_seq2seq_batch'''			) else LegacySeqaSeqDataset
				)
				lowerCAmelCase												=  False
				lowerCAmelCase												=  self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
				if self.hparams.eval_max_gen_length is not None:
					lowerCAmelCase												=  self.hparams.eval_max_gen_length
				else:
					lowerCAmelCase												=  self.model.config.max_length
				lowerCAmelCase												=  self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






			def 					UpperCAmelCase							(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	Dict[str, torch.Tensor]			)       ->	Dict[str, List[str]]:





				"""simple docstring"""


				lowerCAmelCase												=  {
				    k: self.tokenizer.batch_decode(v.tolist()			) if '''mask''' not in k else v.shape for k, v in batch.items()
				}
				save_json(SCREAMING_SNAKE_CASE_      ,Path(self.output_dir			) / '''text_batch.json'''			)
				save_json({k: v.tolist() for k, v in batch.items()}      ,Path(self.output_dir			) / '''tok_batch.json'''			)

				lowerCAmelCase												=  True
				return readable_batch






			def 					UpperCAmelCase							(self  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	str:





				"""simple docstring"""


				return self.model(SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	List[int]			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.batch_decode(
				    SCREAMING_SNAKE_CASE_      ,skip_special_tokens=SCREAMING_SNAKE_CASE_      ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_			)
				return lmap(str.strip      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	dict			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.pad_token_id
				lowerCAmelCase							,       lowerCAmelCase												=  batch['''input_ids'''], batch['''attention_mask''']
				lowerCAmelCase												=  batch['''labels''']
				if isinstance(self.model      ,SCREAMING_SNAKE_CASE_			):
					lowerCAmelCase												=  self.model._shift_right(SCREAMING_SNAKE_CASE_			)
				else:
					lowerCAmelCase												=  shift_tokens_right(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
					lowerCAmelCase												=  decoder_input_ids
					self.save_readable_batch(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self(SCREAMING_SNAKE_CASE_      ,attention_mask=SCREAMING_SNAKE_CASE_      ,decoder_input_ids=SCREAMING_SNAKE_CASE_      ,use_cache=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  outputs['''logits''']
				if self.hparams.label_smoothing == 0:
					# Same behavior as modeling_bart.py, besides ignoring pad_token_id
					lowerCAmelCase												=  nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE_			)

					assert lm_logits.shape[-1] == self.vocab_size
					lowerCAmelCase												=  ce_loss_fct(lm_logits.view(-1      ,lm_logits.shape[-1]			)      ,tgt_ids.view(-1			)			)
				else:
					lowerCAmelCase												=  nn.functional.log_softmax(SCREAMING_SNAKE_CASE_      ,dim=-1			)
					lowerCAmelCase							,       lowerCAmelCase												=  label_smoothed_nll_loss(
					    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,self.hparams.label_smoothing      ,ignore_index=SCREAMING_SNAKE_CASE_			)
				return (loss,)






			@property
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				return self.tokenizer.pad_token_id






			def 					UpperCAmelCase							(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	List[str]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self._step(SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  dict(zip(self.loss_names      ,SCREAMING_SNAKE_CASE_			)			)
				# tokens per batch
				lowerCAmelCase												=  batch['''input_ids'''].ne(self.pad			).sum() + batch['''labels'''].ne(self.pad			).sum()
				lowerCAmelCase												=  batch['''input_ids'''].shape[0]
				lowerCAmelCase												=  batch['''input_ids'''].eq(self.pad			).sum()
				lowerCAmelCase												=  batch['''input_ids'''].eq(self.pad			).float().mean()
				# TODO(SS): make a wandb summary metric for this
				return {"loss": loss_tensors[0], "log": logs}






			def 					UpperCAmelCase							(self  :	str      ,SCREAMING_SNAKE_CASE_  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	Dict			)       ->	Dict:





				"""simple docstring"""


				return self._generative_step(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[int]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]="val"			)       ->	Dict:





				"""simple docstring"""


				self.step_count += 1
				lowerCAmelCase												=  {k: torch.stack([x[k] for x in outputs]			).mean() for k in self.loss_names}
				lowerCAmelCase												=  losses['''loss''']
				lowerCAmelCase												=  {
				    k: np.array([x[k] for x in outputs]			).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
				}
				lowerCAmelCase												=  (
				    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
				)
				lowerCAmelCase												=  torch.tensor(SCREAMING_SNAKE_CASE_			).type_as(SCREAMING_SNAKE_CASE_			)
				generative_metrics.update({k: v.item() for k, v in losses.items()}			)
				losses.update(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  {F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
				lowerCAmelCase												=  self.step_count
				self.metrics[prefix].append(SCREAMING_SNAKE_CASE_			)  # callback writes this to self.metrics_save_path
				lowerCAmelCase												=  flatten_list([x['''preds'''] for x in outputs]			)
				return {
				    "log": all_metrics,
				    "preds": preds,
				    F"""{prefix}_loss""": loss,
				    F"""{prefix}_{self.val_metric}""": metric_tensor,
				}






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]			)       ->	Dict:





				"""simple docstring"""


				return calculate_rouge(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	dict			)       ->	dict:





				"""simple docstring"""


				lowerCAmelCase												=  time.time()

				# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
				lowerCAmelCase												=  self.model.generate(
				    batch['''input_ids''']      ,attention_mask=batch['''attention_mask''']      ,use_cache=SCREAMING_SNAKE_CASE_      ,decoder_start_token_id=self.decoder_start_token_id      ,num_beams=self.eval_beams      ,max_length=self.eval_max_length      ,)
				lowerCAmelCase												=  (time.time() - ta) / batch['''input_ids'''].shape[0]
				lowerCAmelCase												=  self.ids_to_clean_text(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.ids_to_clean_text(batch['''labels''']			)
				lowerCAmelCase												=  self._step(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  dict(zip(self.loss_names      ,SCREAMING_SNAKE_CASE_			)			)
				lowerCAmelCase												=  self.calc_generative_metrics(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  np.mean(lmap(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)
				base_metrics.update(gen_time=SCREAMING_SNAKE_CASE_      ,gen_len=SCREAMING_SNAKE_CASE_      ,preds=SCREAMING_SNAKE_CASE_      ,target=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)
				return base_metrics






			def 					UpperCAmelCase							(self  :	Dict      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	Any			)       ->	Tuple:





				"""simple docstring"""


				return self._generative_step(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Tuple:





				"""simple docstring"""


				return self.validation_epoch_end(SCREAMING_SNAKE_CASE_      ,prefix='''test'''			)






			def 					UpperCAmelCase							(self  :	int      ,SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	SeqaSeqDataset:





				"""simple docstring"""


				lowerCAmelCase												=  self.n_obs[type_path]
				lowerCAmelCase												=  self.target_lens[type_path]
				lowerCAmelCase												=  self.dataset_class(
				    self.tokenizer      ,type_path=SCREAMING_SNAKE_CASE_      ,n_obs=SCREAMING_SNAKE_CASE_      ,max_target_length=SCREAMING_SNAKE_CASE_      ,**self.dataset_kwargs      ,)
				return dataset






			def 					UpperCAmelCase							(self  :	Dict      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	bool = False			)       ->	DataLoader:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_dataset(SCREAMING_SNAKE_CASE_			)

				if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
					lowerCAmelCase												=  dataset.make_sortish_sampler(SCREAMING_SNAKE_CASE_      ,distributed=self.hparams.gpus > 1			)
					return DataLoader(
					    SCREAMING_SNAKE_CASE_      ,batch_size=SCREAMING_SNAKE_CASE_      ,collate_fn=dataset.collate_fn      ,shuffle=SCREAMING_SNAKE_CASE_      ,num_workers=self.num_workers      ,sampler=SCREAMING_SNAKE_CASE_      ,)

				elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
					lowerCAmelCase												=  dataset.make_dynamic_sampler(
					    self.hparams.max_tokens_per_batch      ,distributed=self.hparams.gpus > 1			)
					return DataLoader(
					    SCREAMING_SNAKE_CASE_      ,batch_sampler=SCREAMING_SNAKE_CASE_      ,collate_fn=dataset.collate_fn      ,num_workers=self.num_workers      ,)
				else:
					return DataLoader(
					    SCREAMING_SNAKE_CASE_      ,batch_size=SCREAMING_SNAKE_CASE_      ,collate_fn=dataset.collate_fn      ,shuffle=SCREAMING_SNAKE_CASE_      ,num_workers=self.num_workers      ,sampler=SCREAMING_SNAKE_CASE_      ,)






			def 					UpperCAmelCase							(self  :	str			)       ->	DataLoader:





				"""simple docstring"""


				lowerCAmelCase												=  self.get_dataloader('''train'''      ,batch_size=self.hparams.train_batch_size      ,shuffle=SCREAMING_SNAKE_CASE_			)
				return dataloader






			def 					UpperCAmelCase							(self  :	Dict			)       ->	DataLoader:





				"""simple docstring"""


				return self.get_dataloader('''val'''      ,batch_size=self.hparams.eval_batch_size			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	DataLoader:





				"""simple docstring"""


				return self.get_dataloader('''test'''      ,batch_size=self.hparams.eval_batch_size			)






			@staticmethod
			def 					UpperCAmelCase							(SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				add_generic_args(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				parser.add_argument(
				    '''--max_source_length'''      ,default=1_024      ,type=SCREAMING_SNAKE_CASE_      ,help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )      ,)
				parser.add_argument(
				    '''--max_target_length'''      ,default=56      ,type=SCREAMING_SNAKE_CASE_      ,help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )      ,)
				parser.add_argument(
				    '''--val_max_target_length'''      ,default=142      ,type=SCREAMING_SNAKE_CASE_      ,help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )      ,)
				parser.add_argument(
				    '''--test_max_target_length'''      ,default=142      ,type=SCREAMING_SNAKE_CASE_      ,help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )      ,)
				parser.add_argument('''--freeze_encoder'''      ,action='''store_true'''			)
				parser.add_argument('''--freeze_embeds'''      ,action='''store_true'''			)
				parser.add_argument('''--sortish_sampler'''      ,action='''store_true'''      ,default=SCREAMING_SNAKE_CASE_			)
				parser.add_argument('''--overwrite_output_dir'''      ,action='''store_true'''      ,default=SCREAMING_SNAKE_CASE_			)
				parser.add_argument('''--max_tokens_per_batch'''      ,type=SCREAMING_SNAKE_CASE_      ,default=SCREAMING_SNAKE_CASE_			)
				parser.add_argument('''--logger_name'''      ,type=SCREAMING_SNAKE_CASE_      ,choices=['''default''', '''wandb''', '''wandb_shared''']      ,default='''default'''			)
				parser.add_argument('''--n_train'''      ,type=SCREAMING_SNAKE_CASE_      ,default=-1      ,required=SCREAMING_SNAKE_CASE_      ,help='''# examples. -1 means use all.'''			)
				parser.add_argument('''--n_val'''      ,type=SCREAMING_SNAKE_CASE_      ,default=500      ,required=SCREAMING_SNAKE_CASE_      ,help='''# examples. -1 means use all.'''			)
				parser.add_argument('''--n_test'''      ,type=SCREAMING_SNAKE_CASE_      ,default=-1      ,required=SCREAMING_SNAKE_CASE_      ,help='''# examples. -1 means use all.'''			)
				parser.add_argument(
				    '''--task'''      ,type=SCREAMING_SNAKE_CASE_      ,default='''summarization'''      ,required=SCREAMING_SNAKE_CASE_      ,help='''# examples. -1 means use all.'''			)
				parser.add_argument('''--label_smoothing'''      ,type=SCREAMING_SNAKE_CASE_      ,default=0.0      ,required=SCREAMING_SNAKE_CASE_			)
				parser.add_argument('''--src_lang'''      ,type=SCREAMING_SNAKE_CASE_      ,default=''''''      ,required=SCREAMING_SNAKE_CASE_			)
				parser.add_argument('''--tgt_lang'''      ,type=SCREAMING_SNAKE_CASE_      ,default=''''''      ,required=SCREAMING_SNAKE_CASE_			)
				parser.add_argument('''--eval_beams'''      ,type=SCREAMING_SNAKE_CASE_      ,default=SCREAMING_SNAKE_CASE_      ,required=SCREAMING_SNAKE_CASE_			)
				parser.add_argument(
				    '''--val_metric'''      ,type=SCREAMING_SNAKE_CASE_      ,default=SCREAMING_SNAKE_CASE_      ,required=SCREAMING_SNAKE_CASE_      ,choices=['''bleu''', '''rouge2''', '''loss''', None]			)
				parser.add_argument('''--eval_max_gen_length'''      ,type=SCREAMING_SNAKE_CASE_      ,default=SCREAMING_SNAKE_CASE_      ,help='''never generate more than n tokens'''			)
				parser.add_argument('''--save_top_k'''      ,type=SCREAMING_SNAKE_CASE_      ,default=1      ,required=SCREAMING_SNAKE_CASE_      ,help='''How many checkpoints to save'''			)
				parser.add_argument(
				    '''--early_stopping_patience'''      ,type=SCREAMING_SNAKE_CASE_      ,default=-1      ,required=SCREAMING_SNAKE_CASE_      ,help=(
				        '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
				        ''' val_check_interval will effect it.'''
				    )      ,)
				return parser





class 			lowercase					( lowercase__		):
			lowercase        =     '''translation'''
			lowercase        =     ['''loss''']
			lowercase        =     ['''bleu''']
			lowercase        =     '''bleu'''






			def __init__(self  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	Dict      ,**SCREAMING_SNAKE_CASE_  :	List[str]			)       ->	int:





				"""simple docstring"""


				super().__init__(SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  hparams.src_lang
				lowerCAmelCase												=  hparams.tgt_lang






			def 					UpperCAmelCase							(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	List[str]			)       ->	dict:





				"""simple docstring"""


				return calculate_bleu(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)





def __magic_name__	(				_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		int=None			)					->						SummarizationModule:

	'''simple docstring'''




	Path(args.output_dir			).mkdir(exist_ok=_lowerCamelCase			)
	check_output_dir(_lowerCamelCase,					expected_items=3			)

	if model is None:
		if "summarization" in args.task:
			lowerCAmelCase												=  SummarizationModule(_lowerCamelCase			)
		else:
			lowerCAmelCase												=  TranslationModule(_lowerCamelCase			)
	lowerCAmelCase												=  Path(args.data_dir			).name
	if (
	    args.logger_name == "default"
	    or args.fast_dev_run
	    or str(args.output_dir			).startswith('''/tmp'''			)
	    or str(args.output_dir			).startswith('''/var'''			)
	):
		lowerCAmelCase												=  True  # don't pollute wandb logs unnecessarily
	elif args.logger_name == "wandb":
		from pytorch_lightning.loggers import WandbLogger

		lowerCAmelCase												=  os.environ.get('''WANDB_PROJECT''',					_lowerCamelCase			)
		lowerCAmelCase												=  WandbLogger(name=model.output_dir.name,					project=_lowerCamelCase			)

	elif args.logger_name == "wandb_shared":
		from pytorch_lightning.loggers import WandbLogger

		lowerCAmelCase												=  WandbLogger(name=model.output_dir.name,					project=F"""hf_{dataset}"""			)

	if args.early_stopping_patience >= 0:
		lowerCAmelCase												=  get_early_stopping_callback(model.val_metric,					args.early_stopping_patience			)
	else:
		lowerCAmelCase												=  False

	lowerCAmelCase												=  args.val_metric == '''loss'''
	lowerCAmelCase												=  generic_train(
	    _lowerCamelCase,					_lowerCamelCase,					logging_callback=SeqaSeqLoggingCallback(),					checkpoint_callback=get_checkpoint_callback(
	        args.output_dir,					model.val_metric,					args.save_top_k,					_lowerCamelCase			),					early_stopping_callback=_lowerCamelCase,					logger=_lowerCamelCase,					)
	pickle_save(model.hparams,					model.output_dir / '''hparams.pkl'''			)
	if not args.do_predict:
		return model

	lowerCAmelCase												=  ''''''
	lowerCAmelCase												=  sorted(glob.glob(os.path.join(args.output_dir,					'''*.ckpt'''			),					recursive=_lowerCamelCase			)			)
	if checkpoints:
		lowerCAmelCase												=  checkpoints[-1]
		lowerCAmelCase												=  checkpoints[-1]
	trainer.logger.log_hyperparams(model.hparams			)

	# test() without a model tests using the best checkpoint automatically
	trainer.test()
	return model


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					UpperCAmelCase        =				pl.Trainer.add_argparse_args(parser)
					UpperCAmelCase        =				SummarizationModule.add_model_specific_args(parser, os.getcwd())

					UpperCAmelCase        =				parser.parse_args()

					main(args)




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}



class 			lowercase					( lowercase__		):
			lowercase        =     '''openai-gpt'''
			lowercase        =     {
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}






			def __init__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	str=40_478      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=512      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=768      ,SCREAMING_SNAKE_CASE_  :	Dict=12      ,SCREAMING_SNAKE_CASE_  :	Dict=12      ,SCREAMING_SNAKE_CASE_  :	int="gelu"      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.1      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=1e-5      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.02      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]="cls_index"      ,SCREAMING_SNAKE_CASE_  :	str=True      ,SCREAMING_SNAKE_CASE_  :	Any=None      ,SCREAMING_SNAKE_CASE_  :	List[Any]=True      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.1      ,**SCREAMING_SNAKE_CASE_  :	int      ,)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  vocab_size
				lowerCAmelCase												=  n_positions
				lowerCAmelCase												=  n_embd
				lowerCAmelCase												=  n_layer
				lowerCAmelCase												=  n_head
				lowerCAmelCase												=  afn
				lowerCAmelCase												=  resid_pdrop
				lowerCAmelCase												=  embd_pdrop
				lowerCAmelCase												=  attn_pdrop
				lowerCAmelCase												=  layer_norm_epsilon
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  summary_type
				lowerCAmelCase												=  summary_use_proj
				lowerCAmelCase												=  summary_activation
				lowerCAmelCase												=  summary_first_dropout
				lowerCAmelCase												=  summary_proj_to_labels
				super().__init__(**SCREAMING_SNAKE_CASE_			)


